Next Article in Journal
Forecasting Transplanted Rice Yield at the Farm Scale Using Moderate-Resolution Satellite Imagery and the AquaCrop Model: A Case Study of a Rice Seed Production Community in Thailand
Previous Article in Journal
Spatial Footprints of Human Perceptual Experience in Geo-Social Media
Open AccessArticle

Adaptive Component Selection-Based Discriminative Model for Object Detection in High-Resolution SAR Imagery

by 1,2,*, 1, 1, 1 and 2,3
1
Electronic Information School, Wuhan University, Wuhan 430072, China
2
State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
3
Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(2), 72; https://doi.org/10.3390/ijgi7020072
Received: 6 December 2017 / Revised: 14 February 2018 / Accepted: 18 February 2018 / Published: 23 February 2018
This paper proposes an innovative Adaptive Component Selection-Based Discriminative Model (ACSDM) for object detection in high-resolution synthetic aperture radar (SAR) imagery. In order to explore the structural relationships between the target and the components, a multi-scale detector consisting of a root filter and several part filters is established, using Histogram of Oriented Gradient (HOG) features to describe the object from different resolutions. To make the detected components of practical significance, the size and anchor position of each component are determined through statistical methods. When training the root model and the corresponding part models, manual annotation is adopted to label the target in the training set. Besides, a penalty factor is introduced to compensate information loss in preprocessing. In the detection stage, the Small Area-Based Non-Maximum Suppression (SANMS) method is utilised for filtering out duplicate results. In the experiments, the aeroplanes in TerraSAR-X SAR images are detected by the ACSDM algorithm and different comparative methods. The results indicate that the proposed method has a lower false alarm rate and can detect the components accurately. View Full-Text
Keywords: adaptive component selection; synthetic aperture radar (SAR); object detection; histogram of oriented gradient (HOG); Small Area-Based Non-Maximum Suppression (SANMS) adaptive component selection; synthetic aperture radar (SAR); object detection; histogram of oriented gradient (HOG); Small Area-Based Non-Maximum Suppression (SANMS)
Show Figures

Figure 1

MDPI and ACS Style

He, C.; Tu, M.; Xiong, D.; Tu, F.; Liao, M. Adaptive Component Selection-Based Discriminative Model for Object Detection in High-Resolution SAR Imagery. ISPRS Int. J. Geo-Inf. 2018, 7, 72.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop